6 accepted papers
State estimation is a crucial component for the successful implementation of robotic systems, relying on sensors such as cameras, LiDAR, and IMUs. However, in real-world scenarios, the performance of these sensors is degraded by challenging environments, e.g. adverse weather conditions and low-light…
In this paper, we address the challenging problem of data association for underwater SLAM through a novel method for sonar image correspondence using learned features. We introduce SONIC (SONar Image Correspondence), a pose-supervised network designed to yield robust feature correspondence capable o…
Underwater robots typically rely on acoustic sensors like sonar to perceive their surroundings. However, these sensors are often inundated with multiple sources and types of noise, which makes using raw data for any meaningful inference with features, objects, or boundary returns very difficult. Whi…
Having accurate localization capabilities is one of the fundamental requirements of autonomous robots. For underwater vehicles, the choices for effective localization are limited due to limitations of GPS use in water and poor environ-mental visibility that makes camera-based methods ineffective. Po…
Simultaneous Localization and Mapping (SLAM) is commonly formulated as an optimization over a graph. A popular approach is the pose graph, which seeks to solve for robots poses that are constrained by pose-to-pose measurements, such as odometry measurements or loop closures. For range sensors, these…
Recent algorithms have demonstrated the feasibility of underwater feature-based SLAM using imaging sonar. But previous methods have either relied on manual feature extraction and correspondence or used prior knowledge of the scene, such as the planar scene assumption. Our proposed system provides a…